An abnormality has been detected based on data acquired time-sequentially (hereinafter, referred to as time series data). For example, in another method, an abnormality is determined whether it occurs based on a feature amount (for example, statistically amount) extracted from data in a normal state and a feature amount extracted from target data.
However, although an abnormality can be determined on whether it occurs by the above methods, it is difficult to determine whether an occurrence cause of the abnormality and a past cause of the abnormality are equal. For example, the time series data as illustrated in FIG. 1 is acquired. In FIG. 1, the value of the horizontal axis represents time, and the value of the vertical axis represents a value of a specific item. In FIG. 1, an abnormal state 11 and an abnormal state 12 occur, and the portions other than the abnormal state 11 and the abnormal state 12 are a normal state. If the above methods are used in the case of the example of FIG. 1, it is possible to detect that the abnormal state 11 and the abnormal state 12 occur. However, it is difficult to detect whether a cause of the abnormal state 11 and a cause of the abnormal state 12 are different (that is, a type of the abnormal state 11 and a type of the abnormal state 12 are different).
On the other hand, as a technique used to check a relation between data such as the time series data, there is known a multidimensional scaling which is a visualization technique of mapping data in a multidimensional space.
Patent Document 1: Japanese Laid-open Patent Publication No. 2011-34208
According to the multidimensional scaling, a type of the abnormality and another type of the abnormality can be visualized in a distinguishable pattern. However, in the multidimensional scaling, when recalculation is performed due to addition of new data, a positional relation between data is changed from a positional relation based on a calculation result at the time when the new data is not added.
FIGS. 2A to 2C is a diagram for describing the multidimensional scaling. In FIGS. 2B and 2C, Point 23 represents a point corresponding to reference data in FIG. 2A. Points other than Point 23 in FIG. 2B correspond to the time series data contained in a frame 21 Points other than Point 23 in FIG. 2C correspond to the time series data contained in a frame 22.
In this way, in the multidimensional scaling, when the input time series data is changed, the positional relation is changed even though the data is the same. Therefore, it is hard to continuously check a relation between the newly acquired data and the already acquired data.